Probabilistic author-topic models for information discovery

By Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T. Griffiths. Knowledge Discovery and Data Mining, 2004

670 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/dcb426cd5f9ca9a0d58e852fb8757202bfc36d37

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Citations in the corpus

Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition (2012, background): “Similarly, there are many related works for non-spatial temporal document collections, for example IN-SPIRE [36], which is a general purpose document analysis system that depicts document clusters on a visual landscape of topics.”
Topic modeling with network regularization (2008, methodology): “Author-topic analysis has been proposed in text mining literature [26, 23, 20].”
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge (2016, background): “The core generative mechanisms of LDA, in particular, have inspired numerous generalizations that account for additional information, such as the authorship (Rosen-Zvi et al., 2004), document labels (Mcauliffe and Blei, 2008), or hierarchical structure (Grifﬁths et al., 2004).”
Analysis and Visualization of Network Data using JUNG (2005, methodology): “The tool was developed in the context of a project at the University of California, Irvine funded by the National Science Foundation which involved the development of algorithms for large relational data sets, such as CiteSeer (White and Smyth (2003), Steyvers, Smyth, Rosen-Zvi, and Griths (2004) ,”
Topic and Role Discovery in Social Networks (2005, methodology): “The Author-Topic (AT) model is a similar Bayesian network, in which each authors’ interests are modeled with a mixture of topics [Steyvers et al., 2004].”
Identifying miRNAs, targets and functions (2012, methodology): “[86] used a generative model to predict FMRMs adopted from the Author-Topic model [94] which was proposed initially in information retrieval.”
Cross-Lingual Latent Topic Extraction (2010, background): “, 2003a; Li and McCallum, 2006), author-topic modeling (Steyvers et al., 2004), contextual topic analysis (Mei and Zhai, 2006), dynamic and correlated topic models (Blei and Lafferty, 2005; Blei and Lafferty, 2006), and opinion analysis (Mei et al.”
Comparing Twitter and Traditional Media Using Topic Models (2011, methodology): “In terms of topic modeling, our model is based on [8] but samples a single topic for a whole sentence.”
Strathprints Institutional Repository Bayesian Latent Variable Models for Collaborative Item Rating Prediction. In: Acm Cikm, 2011-03-31. Bayesian Latent Variable Models for Collaborative Item Rating Prediction (methodology): “Many useful models have used LDA as a base to work from, for example the Author-Topic model [18] which models academic publications and citations.”
Cross-domain collaboration recommendation (2012, background): “How do people collaborate across different domains? And what are the hottest topics on which people from different domains tend to collaborate? Recently, probabilistic topic models have been successfully applied to multiple text mining tasks to extract topics from text [4, 15, 27].”
Data Mining for Web Personalization (2007, methodology): “Recently, the LDA model has been used in text mining [16], author-topic analysis [131], and collaborative ﬁltering [73].”